Relevant bibliographies by topics / Joint-sparse

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Joint-sparse'

Author: Grafiati
Published: 3 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "Joint-sparse"

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Zhang, Dong, Yongshun Zhang, and Cunqian Feng. "Joint-2D-SL0 Algorithm for Joint Sparse Matrix Reconstruction." International Journal of Antennas and Propagation 2017 (2017): 1–7. http://dx.doi.org/10.1155/2017/6862852.

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Sparse matrix reconstruction has a wide application such as DOA estimation and STAP. However, its performance is usually restricted by the grid mismatch problem. In this paper, we revise the sparse matrix reconstruction model and propose the joint sparse matrix reconstruction model based on one-order Taylor expansion. And it can overcome the grid mismatch problem. Then, we put forward the Joint-2D-SL0 algorithm which can solve the joint sparse matrix reconstruction problem efficiently. Compared with the Kronecker compressive sensing method, our proposed method has a higher computational effici
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Jianyuan Li, and Jihong Guan. "Learning with Joint Sparse Representation." International Journal of Advancements in Computing Technology 4, no. 6 (2012): 184–91. http://dx.doi.org/10.4156/ijact.vol4.issue6.22.

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Liu, Haibiao, Zhihui Lai, and Yudong Chen. "Joint Sparse Neighborhood Preserving Embedding." Journal of Physics: Conference Series 1176 (March 2019): 032023. http://dx.doi.org/10.1088/1742-6596/1176/3/032023.

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Yi, Shuangyan, Zhihui Lai, Zhenyu He, Yiu-ming Cheung, and Yang Liu. "Joint sparse principal component analysis." Pattern Recognition 61 (January 2017): 524–36. http://dx.doi.org/10.1016/j.patcog.2016.08.025.

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Khanna, Saurabh, and Chandra R. Murthy. "Decentralized Joint-Sparse Signal Recovery: A Sparse Bayesian Learning Approach." IEEE Transactions on Signal and Information Processing over Networks 3, no. 1 (2017): 29–45. http://dx.doi.org/10.1109/tsipn.2016.2612120.

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Khanna, Saurabh, and Chandra R. Murthy. "Communication-Efficient Decentralized Sparse Bayesian Learning of Joint Sparse Signals." IEEE Transactions on Signal and Information Processing over Networks 3, no. 3 (2017): 617–30. http://dx.doi.org/10.1109/tsipn.2016.2632041.

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Sima, Haifeng, Pei Liu, Lanlan Liu, Aizhong Mi, and Jianfang Wang. "Sparse Representation Classification Based on Flexible Patches Sampling of Superpixels for Hyperspectral Images." Mathematical Problems in Engineering 2018 (October 2, 2018): 1–10. http://dx.doi.org/10.1155/2018/8264961.

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Aiming at solving the difficulty of modeling on spatial coherence, complete feature extraction, and sparse representation in hyperspectral image classification, a joint sparse representation classification method is investigated by flexible patches sampling of superpixels. First, the principal component analysis and total variation diffusion are employed to form the pseudo color image for simplifying superpixels computing with (simple linear iterative clustering) SLIC model. Then, we design a joint sparse recovery model by sampling overcomplete patches of superpixels to estimate joint sparse c
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Tosic, Ivana, and Sarah Drewes. "Learning Joint Intensity-Depth Sparse Representations." IEEE Transactions on Image Processing 23, no. 5 (2014): 2122–32. http://dx.doi.org/10.1109/tip.2014.2312645.

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Davies, Mike E., and Yonina C. Eldar. "Rank Awareness in Joint Sparse Recovery." IEEE Transactions on Information Theory 58, no. 2 (2012): 1135–46. http://dx.doi.org/10.1109/tit.2011.2173722.

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Lee, Kiryung, Yoram Bresler, and Marius Junge. "Subspace Methods for Joint Sparse Recovery." IEEE Transactions on Information Theory 58, no. 6 (2012): 3613–41. http://dx.doi.org/10.1109/tit.2012.2189196.

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Dissertations / Theses on the topic "Joint-sparse"

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Song, Pingfan. "Multi-modal image processing via joint sparse representations induced by coupled dictionaries." Thesis, University College London (University of London), 2018. http://discovery.ucl.ac.uk/10061963/.

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Real-world image processing tasks often involve various image modalities captured by different sensors. However, given that different sensors exhibit different characteristics, such multi-modal images are typically acquired with different resolutions, different blurring kernels, or even noise levels. In view of the fact that images associated with the same scene share some attributes, such as edges, textures or other primitives, it is natural to ask whether one can improve standard image processing tasks by leveraging the availability of multimodal images. This thesis introduces a sparsity-bas
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Fischer, Doreen [Verfasser]. "Sparse regularization of a joint inversion of gravitational data and normal mode anomalies / Doreen Fischer." Siegen : Universitätsbibliothek der Universität Siegen, 2011. http://d-nb.info/1019880341/34.

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GIOVANNINI, Ambra. "Parallel Large-Scale Edge-Preserving Joint Inversion with PETSc and TAO." Doctoral thesis, Università degli studi di Ferrara, 2011. http://hdl.handle.net/11392/2388807.

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The focus of this thesis is on the study and the parallel implementation of a software package for the Tikhonov's approach to the joint inversion of multidimensional data, based on minimum support regularization and built on top of the well known and widely used high-performance parallel libraries PETSc and TAO. Studying effective methods and implementing efficient codes for a truly joint inversion arouses great interest, because multiple types of observations of the same object can be used at once in a single procedure to recover an estimate of the object itself via non-invasive inspection.
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Walter, Matthew R. "Sparse Bayesian information filters for localization and mapping." Thesis, Massachusetts Institute of Technology, 2008. http://hdl.handle.net/1721.1/46498.

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Thesis (S.M.)--Joint Program in Oceanography/Applied Ocean Science and Engineering (Massachusetts Institute of Technology, Dept. of Mechanical Engineering; and the Woods Hole Oceanographic Institution), 2008.<br>Includes bibliographical references (p. 159-170).<br>This thesis formulates an estimation framework for Simultaneous Localization and Mapping (SLAM) that addresses the problem of scalability in large environments. We describe an estimation-theoretic algorithm that achieves significant gains in computational efficiency while maintaining consistent estimates for the vehicle pose and the
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Mai, Van Khanh. "Méthodes avancées de traitement de la parole et de réduction de bruit pour les terminaux mobiles." Thesis, Ecole nationale supérieure Mines-Télécom Atlantique Bretagne Pays de la Loire, 2017. http://www.theses.fr/2017IMTA0008/document.

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Cette thèse traite d'un des problèmes les plus stimulants dans le traitement de la parole concernant la prothèse auditive, où seulement un capteur est disponible avec de faibles coûts de calcul, de faible utilisation d'énergie et l'absence de bases de données. Basée sur les récents résultats dans les deux estimations statistiques paramétriques et non-paramétriques, ainsi que la représentation parcimonieuse. Cette étude propose quelques techniques non seulement pour améliorer la qualité et l'intelligibilité de la parole, mais aussi pour s'attaquer au débruitage du signal audio en général.La thè
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Chen, Bing-hong, and 陳秉宏. "Table Driven Algorithm for Joint Sparse Form." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/v3hxw9.

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碩士<br>國立中山大學<br>資訊工程學系研究所<br>95<br>In Cryptography, computing a^xb^y mod n is the most important and the most time-consuming calculation The problem can be solved by classical binary method. Later research is based on this basis to increase computational efficiency. Furthermore, Binary signed-digit representation recoding algorithm, the Sparse Form, the DJM recoding method, and the Joint Sparse Form can be used to decrease the number of multiplication by aligning more non-zero bits. Another method is to pre-compute and store the part of the results to decrease the number of computations by sh
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Teng, Luyao. "Research on Joint Sparse Representation Learning Approaches." Thesis, 2019. https://vuir.vu.edu.au/40024/.

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Dimensionality reduction techniques such as feature extraction and feature selection are critical tools employed in artificial intelligence, machine learning and pattern recognitions tasks. Previous studies of dimensionality reduction have three common problems: 1) The conventional techniques are disturbed by noise data. In the context of determining useful features, the noises may have adverse effects on the result. Given that noises are inevitable, it is essential for dimensionality reduction techniques to be robust from noises. 2) The conventional techniques separate the graph learning syst
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Khanna, Saurabh. "Bayesian Techniques for Joint Sparse Signal Recovery: Theory and Algorithms." Thesis, 2018. https://etd.iisc.ac.in/handle/2005/5292.

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This thesis contributes new theoretical results, solution concepts, and algorithms concerning the Bayesian recovery of multiple joint sparse vectors from noisy and underdetermined linear measurements. The thesis is written in two parts. The first part focuses on the recovery of nonzero support of multiple joint sparse vectors from their linear compressive measurements, an important canonical problem in multisensor signal processing. The support recovery performance of a well known Bayesian inference technique called Multiple Sparse Bayesian Learning (MSBL) is analyzed using tools from lar
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Prasad, Ranjitha. "Sparse Bayesian Learning For Joint Channel Estimation Data Detection In OFDM Systems." Thesis, 2015. http://etd.iisc.ac.in/handle/2005/4008.

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Bayesian approaches for sparse signal recovery have enjoyed a long-standing history in signal processing and machine learning literature. Among the Bayesian techniques, the expectation maximization based Sparse Bayesian Learning(SBL) approach is an iterative procedure with global convergence guarantee to a local optimum, which uses a parameterized prior that encourages sparsity under an evidence maximization frame¬work. SBL has been successfully employed in a wide range of applications ranging from image processing to communications. In this thesis, we propose novel, efficient and low-complexi
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Prasad, Ranjitha. "Sparse Bayesian Learning For Joint Channel Estimation Data Detection In OFDM Systems." Thesis, 2015. http://etd.iisc.ernet.in/2005/3997.

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Bayesian approaches for sparse signal recovery have enjoyed a long-standing history in signal processing and machine learning literature. Among the Bayesian techniques, the expectation maximization based Sparse Bayesian Learning(SBL) approach is an iterative procedure with global convergence guarantee to a local optimum, which uses a parameterized prior that encourages sparsity under an evidence maximization frame¬work. SBL has been successfully employed in a wide range of applications ranging from image processing to communications. In this thesis, we propose novel, efficient and low-complexi
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Books on the topic "Joint-sparse"

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Yilmaz, Koray K. New approaches to hydrological prediction in data-sparse regions: Proceedings of symposium HS.2 at the Joint Convention of the International Association of Hydrological Sciences (IAHS) and the International Association of Hydrogeologists (IAH) held in Hyderabad, India, 6-12 September 2009. IAHS Press, 2009.

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Doherty, Michael, Johannes Bijlsma, Nigel Arden, David J. Hunter, and Nicola Dalbeth. Introduction: the comprehensive approach. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780199668847.003.0020.

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This introductory chapter to the section on management of osteoarthritis (OA) emphasizes the need for a full assessment of the patient, not just in terms of joint symptoms and examination findings but a full holistic assessment of the person, including the impact of OA on their life, their illness perceptions of OA, and the presence of comorbidities. An individualized package of care can then be developed. Patients should be fully informed about OA and fully involved in all management decisions. Apart from education, which is an ongoing not one-off process, other core treatments to be consider
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Book chapters on the topic "Joint-sparse"

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Xuan, Qiyun, Si Wang, Yulong Gao, and Junhui Cheng. "Sparse Decomposition Algorithm Based on Joint Sparse Model." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-22968-9_5.

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Liu, Haibiao, Zhihui Lai, and Yudong Chen. "Joint Sparse Locality Preserving Projections." In Lecture Notes in Computer Science. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-73830-7_13.

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Okeya, Katsuyuki, Hidehiro Kato, and Yasuyuki Nogami. "Width-3 Joint Sparse Form." In Information Security, Practice and Experience. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-12827-1_6.

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Duan, Xiping, Jiafeng Liu, and Xianglong Tang. "Weighted Joint Sparse Representation Based Visual Tracking." In Neural Information Processing. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-26555-1_68.

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Liu, Huaping, and Fuchun Sun. "Tactile Object Recognition Using Joint Sparse Coding." In Robotic Tactile Perception and Understanding. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-6171-4_3.

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Nouranian, Saman, Mahdi Ramezani, Ingrid Spadinger, William J. Morris, Septimiu E. Salcudean, and Purang Abolmaesumi. "Automatic Prostate Brachytherapy Preplanning Using Joint Sparse Analysis." In Lecture Notes in Computer Science. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-24571-3_50.

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Luo, Jie, Bin Yang, and Zhongze Chen. "Color Image Restoration via Extended Joint Sparse Model." In Communications in Computer and Information Science. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33506-8_61.

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Sathiya Suntharam, V., Ravikumar Chandu, and D. Palanivel Rajan. "Robust Multimodal Biometric Recognition Based on Joint Sparse Representation." In Lecture Notes in Electrical Engineering. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-7961-5_26.

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Liu, Huaping, and Fuchun Sun. "Visual–Tactile Fusion Object Recognition Using Joint Sparse Coding." In Robotic Tactile Perception and Understanding. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-6171-4_7.

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Yin, Xu, and Ming Meng. "Multiband Based Joint Sparse Representation for Motor Imagery Classification." In Proceedings of the 11th International Conference on Computer Engineering and Networks. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-6554-7_34.

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Conference papers on the topic "Joint-sparse"

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Zhao, Yonghong, Jisong Liu, Junlong wang, and Shuxin Dong. "A joint sparse Bayesian learning for wideband DOA estimation." In Conference on Spectral Technology and Applications (CSTA 2024), edited by Zhe Wang and Hongbin Ding. SPIE, 2024. https://doi.org/10.1117/12.3037518.

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Lu, Peng, Yi Liu, Yuying Mao, et al. "Structured Joint Sparse Discriminative Dictionary Learning for Image Classification." In 2024 China Automation Congress (CAC). IEEE, 2024. https://doi.org/10.1109/cac63892.2024.10865368.

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Ye, Jong Chul, Jong Min Kim, and Yoram Bresler. "Subspace penalized sparse learning for joint sparse recovery." In ICASSP 2013 - 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2013. http://dx.doi.org/10.1109/icassp.2013.6638824.

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Jiang, Wenlan, Tao Zhang, and Huangang Wang. "Joint Sparse Principal Component Analysis Based Roust Sparse Fault Detection." In 2020 IEEE 9th Data Driven Control and Learning Systems Conference (DDCLS). IEEE, 2020. http://dx.doi.org/10.1109/ddcls49620.2020.9275214.

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Li, Lin, Fei Tong, Rustam Stolkin, Jinwen Hu, and Feng Yang. "Joint sparse learning for classification ensemble." In 2017 13th IEEE International Conference on Control & Automation (ICCA). IEEE, 2017. http://dx.doi.org/10.1109/icca.2017.8003205.

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Lee, Okkyun, and Jong Chul Ye. "Joint sparse recovery in inverse scattering." In SPIE Optical Engineering + Applications, edited by Philip J. Bones, Michael A. Fiddy, and Rick P. Millane. SPIE, 2015. http://dx.doi.org/10.1117/12.2188764.

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Rath, Gagan, Christine Guillemot, and Jean-Jacques Fuchs. "Sparse approximations for joint source-channel coding." In 2008 IEEE 10th Workshop on Multimedia Signal Processing (MMSP). IEEE, 2008. http://dx.doi.org/10.1109/mmsp.2008.4665126.

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Zhu, Xiaofeng, Xindong Wu, Wei Ding, and Shichao Zhang. "Feature Selection by Joint Graph Sparse Coding." In Proceedings of the 2013 SIAM International Conference on Data Mining. Society for Industrial and Applied Mathematics, 2013. http://dx.doi.org/10.1137/1.9781611972832.89.

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Liu, Luoluo, Sang Peter Chin, and Trac D. Tran. "JOBS: Joint-Sparse Optimization from Bootstrap Samples." In 2019 IEEE International Symposium on Information Theory (ISIT). IEEE, 2019. http://dx.doi.org/10.1109/isit.2019.8849654.

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Liu, Jianjun, Zebin Wu, Zhiyong Xiao, and Jinlong Yang. "Hyperspectral Pansharpening via Multitask Joint Sparse Representation." In IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2018. http://dx.doi.org/10.1109/igarss.2018.8518362.

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Reports on the topic "Joint-sparse"

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Chen, Yi-Chen, Vishal M. Patel, Sumit Shekhar, Rama Chellappa, and P. Jonathon Phillips. Video-based face recognition via joint sparse representation. National Institute of Standards and Technology, 2013. http://dx.doi.org/10.6028/nist.ir.7906.

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Balk, Ethan M., Kristin J. Konnyu, Wangnan Cao, et al. Schedule of Visits and Televisits for Routine Antenatal Care: A Systematic Review. Agency for Healthcare Research and Quality (AHRQ), 2022. http://dx.doi.org/10.23970/ahrqepccer257.

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Background. The American College of Obstetricians and Gynecologists and the Society for Maternal-Fetal Medicine plan a new evidence-based joint consensus statement to address the preferred visit schedule and the use of televisits for routine antenatal care. This systematic review will support the consensus statement. Methods. We searched PubMed®, Cochrane databases, Embase®, CINAHL®, ClinicalTrials.gov, PsycINFO®, and SocINDEX from inception through February 12, 2022. We included comparative studies from high-income countries that evaluated the frequency of scheduled routine antenatal visits o
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Konnyu, Kristin J., Louise M. Thoma, Monika Reddy Bhuma, et al. Prehabilitation and Rehabilitation for Major Joint Replacement. Agency for Healthcare Research and Quality (AHRQ), 2021. http://dx.doi.org/10.23970/ahrqepccer248.

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Objectives. This systematic review evaluates the rehabilitation interventions for patients who have undergone (or will undergo) total knee arthroplasty (TKA) or total hip arthroplasty (THA) for the treatment of osteoarthritis. We addressed four Key Questions (KQs): comparisons of (1) rehabilitation prior (“prehabilitation”) to TKA versus no prehabilitation, (2) comparative effectiveness of different rehabilitation programs after TKA, (3) prehabilitation prior to THA versus no prehabilitation, (4) comparative effectiveness of different rehabilitation programs after THA. Data sources and review
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EXPERIMENTAL STUDY ON TRUSS TYPE STEEL REINFORCED CONCRETE JOINTS. The Hong Kong Institute of Steel Construction, 2022. http://dx.doi.org/10.18057/icass2020.p.165.

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"This paper presents an experimental study on the structural behavior of two truss type steel reinforced concrete (SRC) joints. The objective is to characterize the mechanical behavior of SRC joints subjected to static loading. The specimens were scaled from a concrete core tube connected to a mega steel truss. Mechanical behavior of the joint zone is extremely complicated due to the complex geometry and interactive forces among the connected members. Monotonic loading tests were carried out through a self-balanced loading system. Sparse cracks were observed under design loads. Spalling concre
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